Personalization based upon social value in online media

ABSTRACT

Embodiments are directed towards personalizing content to be provided to a user. A recommendation score may be determined for a piece of content for a user. The recommendation score may be based on a combination of an intrinsic value and/or a social value of the content to the user. The social value may be calculated based on a combination of an individual social value for each of the user&#39;s friends, which may be determined based on the combination of a social weight, an interest probability, and a recommendation score for the friend. Online personalization of content for a user may provide the user with the tools to be a valuable, appreciated member of the user&#39;s social network. By employing embodiments, as described herein, content may be determined and provided to a user, which may help the user gain the attention of their social circles.

RELATED APPLICATIONS

This utility patent application is a Continuation claiming the benefitunder 35 U.S.C. §120 of allowed U.S. patent application, Ser. No.13/967,976 filed on Aug. 15, 2013, entitled “Personalization Based UponSocial Value in Online Media” which is a non-provisional patentapplication claiming the benefit under 35 U.S.C. §119(e) of U.S.Provisional Patent Application, Ser. No. 61/694,414 filed on Aug. 29,2012, entitled “Personalization Based Upon Social Value in OnlineMedia.” all of which are incorporated herein by reference.

TECHNICAL FIELD

The present invention relates generally to content management, and moreparticularly, to providing content to a user based on a social valuerecommendation score of the content for the user.

BACKGROUND

Today, online media is a world where curated content is king. Brandstypically employ talented editors that become world-class experts intheir field. Their editorial voice and coverage directs the attention ofmillions of readers. In this world, a selection of the cover story maydetermine the topic of interest for millions of readers. However, thisapproach may lack personalization for individual readers. For example,news about a particular celebrity may be provided to a reader as amainstream interest, even though that reader may not want to readarticles about that celebrity. Sometimes, these uninterested readersquickly abandon the media property because they do not quickly see anarticle that is interesting to them. As a result, the loyalty of theuninterested reader to the media property and/or publisher may bediminished, leading the uninterested reader to a different mediaproperty in the future.

At the same time, no only do readers like to read articles about topic,that they are interested in but they may also read articles about othertopics that their friends and/or social network may be interested in. Ifa reader reads an article about a topic that may be of interest to thereader's social network, then the reader may be able to talk to his/herfriends about that article. As a result, the reader may appear tohis/her social network as smarter, more socially valuable person. It iswith respect to these considerations and other, that the presentinvention has been made.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive embodiments of the present invention aredescribed with reference to the following drawings. In the drawings,like reference numerals refer to like parts throughout the variousfigures unless otherwise specified.

For a better understanding of the present invention, reference will bemade to the following Detailed Description, which is to be read inassociation with the accompanying drawings, wherein:

FIG. 1 is a system diagram of an environment in which embodiments of theinvention may be implemented;

FIG. 2 shows an embodiment of a client device that may be included in asystem such as that shown in FIG. 1;

FIG. 3 shows an embodiment of a network device that may be included in asystem such as that shown in FIG. 1;

FIG. 4 illustrates a logical flow diagram generally showing oneembodiment of an overview process for providing content to a user basedon a recommendation score of the content for the user;

FIG. 5 illustrates a logical flow diagram generally showing oneembodiment of a process for determining a social value of content to auser;

FIG. 6 illustrates a logical flow diagram generally showing oneembodiment of a process for determining content to provide to a user;and

FIG. 7 illustrates a use case embodiment of a social network of a userthat may be utilized to determine a recommendation score for a user.

DETAILED DESCRIPTION

Throughout the specification and claims, the following terms take themeanings explicitly associated herein, unless the context clearlydictates otherwise. The phrase “in one embodiment” as used herein doesnot necessarily refer to the same embodiment, though it may.Furthermore, the phrase “in another embodiment” as used herein does notnecessarily refer to a different embodiment, although it may. Thus, asdescribed below, various embodiments of the invention may be readilycombined, without departing from the scope or spirit of the invention.

In addition, as used herein, the term “or” is an inclusive “or”operator, and is equivalent to the term “and/or,” unless the contextclearly dictates otherwise. The term “based on” is not exclusive andallows for being based on additional factors not described, unless thecontext clearly dictates otherwise. In addition, throughout thespecification, the meaning of “a,” “an,” and “the” include pluralreferences. The meaning of“in” includes “in” and “on.”

As used herein, the term “content” refers to digital data that may becommunicated over a network to be remotely displayed by a computingdevice. Non-exhaustive examples of content include but are not limitedto articles, blogs, movies, videos, music, sounds, pictures,illustrations, graphics, images, text, or the like.

As used herein, the phrase “intrinsic value” may refer to a predictivevalue representative of a personal interest to a user for a given pieceof content.

As used herein, the phrase, “social value” may refer to, predictivevalue representative of an interest to a user's social network for agiven piece of content.

As used herein, the term “social network” refers to a concept of anindividual's personal network of friends, family, colleagues, coworkers,and the subsequent connections within those networks. Social networkscan be utilized to find more relevant connections for a variety ofactivities, including, but not limited to dating, job networking,service referrals, content sharing, like-minded individuals, activitypartners, or the like.

An online social network typically comprises a person's set of directand/or indirect personal relationships, including real and virtualprivileges and permissions that users may associate with these people.Direct personal relationships usually include relationships with peoplethe user can communicate or has communicated with directly, includingfamily members, friends, colleagues, coworkers, and other people withwhich the person has had some form of direct contact, such as contact inperson, by telephone, by email, by instant message, by letter, or thelike. These direct personal relationships are sometimes referred to asfirst-degree friends and/or first-degree relationships. First-degreefriends can have varying degrees of closeness, trust, and othercharacteristics.

Indirect personal relationships typically include relationships throughfirst-degree friends to people with whom a person has not had some formof direct or limited direct contact, such as in being cc'd on an e-mailmessage, or the like. For example, a friend of a friend represents anindirect personal relationship. A more extended, indirect relationshipmight be a friend of a, friend of a friend. These indirect relationshipsare sometimes characterized by a degree of separation between thepeople. For instance, a friend of a friend can be characterized as twodegrees of separation or a second-degree friend and/or a second-degreerelationship. Similarly, a friend of a friend of a friend can becharacterized as three degrees of separation or a third-degree friend.And so on.

The following briefly describes embodiments of the invention in order toprovide a basic understanding of some aspects of the invention. Thisbrief description is not intended as an extensive overview. It is notintended to identify key or critical elements, or to delineate orotherwise narrow the scope. Its purpose is merely to present someconcepts in a simplified form as a prelude to the more detaileddescription that is presented later.

Briefly stated, various embodiments are directed to personalizingcontent to be provided to a user. In at least one embodiment, arecommendation score may be determined for a piece of content for auser. The recommendation score may be based on a combination of anintrinsic value and/or a social value of the content to the user. Theintrinsic value may be the interest, enjoyment, and/or value that theuser nay receive from the content. The social value may be an interestto a user's social network for a given piece of content.

In some embodiments, the social value may be calculated based on acombination of an individual social value for each of the user's firstdegree friends. The individual social value of a friend may bedetermined based on the combination of a social weight, an interestprobability, and a recommendation score for the friend. Accordingly, therecommendation score may be a recursive function based on the user'ssocial network.

Online personalization of content for a user may provide the user withthe tools to be a valuable, appreciated member of the user's socialnetwork. By employing embodiments, as described herein, content may bedetermined and provided to a user, which may help the user gain theattention of their social circles. Instead of trying to capture anddirect the user's attention (e.g., by providing a user a publisherselected headline article), the publisher becomes a back-stage prompter,helping provide the user with content that may make them the center ofattention among their social networks. Each time a publisher providescontent to a user that can be used to make that user more valuablewithin his/her social network, that user's loyalty to the publisher mayincrease.

Illustrative Operating Environment

FIG. 1 shows components of one embodiment of an environment in whichembodiments of the invention may be practiced. Not all of the componentsmay be required to practice the invention, and variations in thearrangement and type of the components may be made without departingfrom the spirit or scope of the invention. As shown, system 100 of FIG.1 includes local area networks (LANs)/wide area networks(WANs)—(network) 110, wireless network 108, client devices 102-105,Score Generator Server Device (SGSD) 112. Content Selection ServerDevice (CSSD) 114, and Logging Agent Server Device (LASD) 116.

At least one embodiment of client devices 102-105 is described in moredetail below in conjunction with FIG. 2. In one embodiment, at leastsome of client devices 102-105 may operate over a wined and/or wirelessnetwork, such as networks 110 and/or 108. Generally, client devices102-105 may include virtually any computing device capable ofcommunicating over a network to send and receive information, performvarious online activities, offline actions, or the like. In oneembodiment, one or more of client devices 102-105 may be configured tooperate within a business or other entity to perform a variety ofservices for the business or other entity. For example, client devices102-105 may be configured to operate as a web server, an accountingserver, a production server, an inventory server, or the like. However,client devices 102-105 are not constrained to these services and mayalso be employed, for example, as ran end-user computing node, in otherembodiments, it should be recognized that more or less client devicesmay be included within a system much as described herein, andembodiments are therefore not constrained by the number or type ofclient devices employed.

Devices that may operate as client device 102 may include devices thattypically connect using a wired or wireless communications medium suchas personal computers, multiprocessor systems, microprocessor-based orprogrammable electronic devices, network PCs, or the like. In someembodiments, client devices 102-105 may include virtually any portablepersonal computing device capable of connecting to another computingdevice and receiving information such as, laptop computer 103, smartmobile telephone 104, and tablet computers 105, and the like. However,portable computing devices are not so limited and may also include otherportable devices such as cellular telephones, display pagers, radiofrequency (RF) devices, infrared (IR) devices. Personal DigitalAssistants (PDAs), handheld computers, wearable computers, integrateddevices combining one or more of the preceding devices, and the like. Assuch, client devices 102-105 typically range widely in terms ofcapabilities and features. Moreover, client devices 102-105 may accessvarious computing applications, including a browser, or other web-basedapplication.

A web-enabled client device may include a browser application that isconfigured to receive and to send web pages, web-based messages, and thelike. The browser application may be configured to receive and displaygraphics, text, multimedia, and the like, employing virtually anyweb-based language, including a wireless application protocol messages(WAP), and the like. In one embodiment, the browser application isenabled to employ Handheld Device Markup Language (HDML), WirelessMarkup Language (WML), WMLScript, JavaScript, Standard GeneralizedMarkup Language (SGML). HyperText Markup Language (HTML), eXtensibleMarkup Language (XML), and the like, to display and send a message. Inone embodiment, a user of the client device may employ the browserapplication to perform various activities over a network (online).However, another application may also be used to perform various onlineactivities.

Client devices 102-105 also may include at least one other clientapplication that is configured to receive and/or send content betweenanother computing device. The client application may include acapability to send and/or receive content, or the like. The clientapplication may further provide information that identifies itself,including a type, capability, name, and the like. In one embodiment,client devices 102-105 may uniquely identify themselves though any of avariety of mechanisms, including an Internet Protocol (IP) address, aphone number, Mobile Identification Number (MIN), an electronic serialnumber (ESN), or other device identifier. Such information may beprovided in a network packet, or the like, sent between other clientdevices, SGSD 112, CSSD 114, LASD 116, or other computing devices.

Client devices 102-105 may further be configured to include a clientapplication that enables an end-user to log into an end-user accountthat may be managed by another computing device, such as SGSD 112, CSSD114, LASD 116, or the like. Such end-user account, in one non-limitingexample, may be configured to enable the end-user to manage one or moteonline activities, including in one non-limiting example, searchactivities, social networking activities, browse various websites,communicate with other users, or the like. However, participation insuch online activities may also be performed without logging into theend-user account.

Wireless network 108 is configured to couple client devices 103-105 andits components with network 110. Wireless net work 108 may include anyof a variety of wireless sub-networks that may further overlaystand-alone ad-hoc networks, and the like, to provide aninfrastructure-oriented connection for client devices 103-105. Suchsub-networks may include mesh networks, Wireless LAN (WLAN) networks,cellular networks, and the like. In one embodiment, the system mayinclude more than one wireless network.

Wireless network 108 may further include an autonomous system ofterminals, gateways, routers, and the like connected by wireless radiolinks, and the like. These connectors may be configured to move freelyand randomly and organize themselves arbitrarily, such that the topologyof wireless network 108 may change rapidly.

Wireless network 108 may further employ a plurality of accesstechnologies including 2nd (2G), 3rd (3G), 4th (4G) 5th (5G) generationradio access for cellular systems, WLAN, Wireless Router (WR) mesh, andthe like. Access technologies such as 2G, 3G, 4G, 5G, and future accessnetworks may enable wide area coverage for mobile devices, such asclient devices 103-105 with various degrees of mobility. In onenon-limiting example, wireless network 108 may enable a radio connectionthrough at radio network access such as Global System for Mobilcommunication (GSM), General Packet Radio Services (GPRS), Enhanced DataGSM Environment (EDGE), code division multiple access (CDMA), timedivision multiple access (TDMA), Wideband Code Division Multiple Access(WCDMA), High Speed Downlink Packet Access (HSDPA), Long Term Evolution(LTE), and the like. In essence, wireless network 108 may includevirtually any wireless communication mechanism by which information maytravel between client devices 103-105 and another computing device,network, and the like.

Network 110 is configured to couple network devices with other computingdevices, including. SGSD 112. CSSD 114, LASD 116, client device 102, andclient devices 103-105 through wireless network 108. Network 110 isenabled to employ any form of computer readable media for communicatinginformation from one electronic device to another. Also, network 110 caninclude the Internet in addition to local area networks (LANs), widearea networks (WANs), direct connections, such as through a universalserial bus (USB) port, other forms of computer-readable media, or anycombination thereof. On an interconnected set of LANs, including thosebased on differing architectures and protocols, a router acts as a linkbetween LANs, enabling messages to be sent from one to another. Inaddition, communication links within LANs typically include twisted wirepair or coaxial cable, while communication links between networks mayutilize analog telephone lines, full or fractional dedicated digitallines including T1, T2, T3, and T4, and/or other carrier mechanismsincluding, for example, E-carriers, Integrated Services Digital Networks(ISDNs), Digital Subscriber Lines (DSLs), wireless links includingsatellite links, or other communications links known to those skilled inthe art. Moreover, communication links may further employ any of avariety of digital signaling technologies, including without limit, forexample, DS-0, DS-1, DS-2, DS-3, DS-4, OC-3, OC-12, OC-48, or the like.Furthermore, remote computers and other related electronic devices couldbe remotely connected to either LANs or WANs via a modem and temporarytelephone link. In one embodiment, network 110 may be configured totransport information of an Internet Protocol (IP). In essence, network110 includes any communication method by which information may travelbetween computing devices.

Additionally, communication media typically embodies computer readableinstructions, data structures, program modules, or other transportmechanism and includes any information delivery media. By way ofexample, communication media includes wired media such as twisted pair,coaxial cable, fiber optics, wave guides, and other wired media andwireless media such as acoustic, RF, infrared, and other wireless media.

One embodiment of SGSD 112 is described in more detail below inconjunction with FIG. 3. Briefly, however, SGSD 112 includes virtuallyany network device capable of determining a recommendation score ofcontent for one or more users. In at least one embodiment, SGSD 112 maycalculate the recommendation score based on a determined intrinsic valueand social value of content to the user.

In some embodiments. SGSD 112 may receive possible content from CSSD114. In other embodiments. SGSD 112 may provide CSSD 114 the determinedrecommendation scores. In at least one embodiment, SGSD 112 maycommunicate with LASD 116 to obtain historical data associated with auser, which may be utilized to determine the recommendation score ofcontent for the user.

Devices that may be arranged to operate as SGSD 112 include variousnetwork devices, including, but not limited to personal computers,desktop computers, multiprocessor systems, microprocessor-based orprogrammable consumer electronics, network PCs, server devices, networkappliances, and the like.

Although FIG. 1 illustrates SGSD 112 as a single computing device, theinvention is not so limited. For example, one or more functions of theSGSD 112 may be distributed across one or more distinct network devices.Moreover. SGSD 112 is nor limited to a particular configuration. Thus,in one embodiment, SGSD 112 may contain a plurality of network devicesto generate recommendation scores for content. In another embodiment,SGSD 112 may contain a plurality of network devices that operate using amaster/slave approach, where one of the plurality of network devices ofSGSD 112 operates to manage and/or otherwise coordinate operations ofthe other network devices. In other embodiments, the SGSD 112 mayoperate as a plurality of network devices within a cluster architecture,a peer-to-peer architecture, and/or even within a cloud architecture.Thus, the invention is not to be construed as being limited to a singleenvironment, and other configurations, and architectures are alsoenvisaged. In at least one embodiment, each of a plurality of SGSD 112may be geographically distributed. In another embodiment. SGSD 112 mayinclude a different computing device thr monitoring tags in each of aplurality of different tag states.

One embodiment of CSSD 114 is described in more detail below inconjunction with FIG. 3. Briefly, however. CSSD 114 includes virtuallyany network device capable of selecting content to provide to a userbased on recommendation scores of the content for the user. In at leastone embodiment, CSSD 114 may provide SGSD 112 with possible content toprovide to a particular user of client devices 102-105. In someembodiments, CSSD 114 may receive recommendation scores from SGSD 112based on the particular user and the possible content. CSSD 114 may thenemploy the recommendation scores to determine which content to provideto the user. In at least one embodiment, CSSD 114 may rank therecommendation scores for a plurality of content to determine an orderfor providing content to a user. CSSD 114 may, in other embodiments,also provide other content and/or low ranked content to the user to beused to for later recommendation score generation.

Devices that may be arranged to operate as CSSD 114 include variousnetwork devices, including, but not limited to personal computers,desktop computers, multiprocessor systems, microprocessor-based orprogrammable consumer electronics, network PCs, server devices, networkappliances, and the like.

Although FIG. 1 illustrates CSSD 114 as a single computing device, theinvention is not so limited. For example, one or more functions of theCSSD 114 may be distributed across one or more distinct network devices.Moreover. CSSD 114 is not limited to a particular configuration. Thus,in one embodiment. CSSD 114 may contain a plurality of network devicesto select content to provide to a user based on determinedrecommendation scores of the content. In another embodiment, CSSD 114may contain a plurality of network devices that operate using amaster/slave approach, where one of the plurality of network devices ofCSSD 114 operates to manage and/or otherwise coordinate operations ofthe other network devices. In other embodiments, the CSSD 114 mayoperate as a plurality of network devices within a cluster architecture,a peer-to-peer architecture, and/or even within a cloud architecture.Thus, the invention is not to be construed as being limited to a singleenvironment, and other configurations, and architectures are alsoenvisaged. In at least one embodiment, each of a plurality of CSSD 114may be geographically distributed. In another embodiment, CSSD 114 mayinclude a different computing device for monitoring tags in each of aplurality of different tag states.

One embodiment of LASD 116 is described in more detail below inconjunction with FIG. 3. Briefly, however, LASD 116 includes virtuallyany network device capable of logging and/or recording data associatedwith users of client devices 102-105. In some embodiments, the loggeddata may include information about whether the user accessed aparticular piece of content, how long the user accessed the content, didthe user share the content with another user, or the like.

In at least one embodiment, LASD 116 may collect and store data for eachof a plurality of individual users. In some embodiments, LASD 116 mayidentify and/or distinguish users based on a unique identifier of theuser, such as if the user is a subscriber to a website, a cookiepreviously provided to the client device of the user, a deviceidentifier, or the like. In at least one embodiment, LASD 116 may beenabled to utilize third party subscriptions to track individual users.In some embodiments. LASD 116 may provide the logged data to SGSD 112.In some embodiments, LASD 116 and CSSD 114 may be associated with sameand/or different publishers. In at least one embodiment, LASD 116 and/orCSSD 114 may be referred to and/or associated with third partypublishers.

Devices that may be arranged to operate as LASD 116 include variousnetwork devices, including, but not limited to personal computers,desktop computers, multiprocessor systems, microprocessor-based orprogrammable consumer electronics, network PCs, server devices, networkappliances, and the like. In at least one embodiment. LASD 116 may beassociated with a publisher. In some embodiments. LASD 116 may include aplurality of different devices, where each device may be associated witha different publisher.

Although FIG. 11 illustrates LASD 116 as a single computing device, theinvention is not so limited. For example, one or more functions of theLASD 116 may be distributed across one of more distinct network devices.Moreover, LASD 116 is not limited to a particular configuration. Inanother embodiment. LASD 116 may contain a plurality of network devicesthat operate using a master/slave approach, where one of the pluralityof network devices of LASD 116 operates to manage and/or otherwisecoordinate operations of the other network device. In other embodiments,the LASD 116 may operate as a plurality of network devices within acluster architecture, a peer-to-peer architecture, and/or even within acloud architecture. Thus, the invention is not to be construed as beinglimited to a single environment, and other configurations, andarchitectures are also envisaged. In at least one embodiment, each of aplurality of LASD 116 may be geographically distributed. In anotherembodiment, LASD 116 may include a different computing device formonitoring tags in each of a plurality of different tag states.

Illustrative Client Device

FIG. 2 shows one embodiment of client device 200 that may be included ina system implementing embodiments of the invention. Client device 200may include many more or less components than those shown in FIG. 2.However, the components shown are sufficient to disclose an illustrativeembodiment for practicing the present invention. Client device 200 mayrepresent, for example, one embodiment of at least one of client devices102-105 of FIG. 1.

As shown in the figure, client device 200 includes a processor 202 incommunication with a mass memory 226 via a bus 234. In some embodiments,processor 202 may include one or more central processing units (CPU).Client device 200 also includes a power supply 228, one or more networkinterfaces 236, an audio interface 238, a display 240, a keypad 242, anilluminator 244, a video interface 246, an input/output interface 248, ahaptic interface 250, and a global positioning system (GPS) receiver232.

Power supply 228 provides power to client device 200. A rechargeable ornon-rechargeable battery may be used to provide power. The power nayalso be provided by an external power source, such as an alternatingcurrent (AC) adapter or a powered docking cradle that supplements and/orrecharges a battery.

Client device 200 may optionally communicate with a base station (notshown), or directly with another computing device. Network interface 236includes circuitry for coupling client device 200 to one or morenetworks, and is constructed for use with one or more communicationprotocols and technologies including, but not limited to, GSM, CDMA,TDMA, GPRS, EDGE, WCDMA, HSDPA, LTE, user datagram protocol (UDP),transmission control protocol/Internet protocol (TCP/IP), short messageservice (SMS), WAP, ultra wide hand (UWB), IEEE 802.16 WorldwideInteroperability for Microwave Access (WiMax), session initiatedprotocol/real-time transport protocol (SIP/RTP), or any of a variety ofother wireless communication protocols. Network interface 236 issometimes known as a transceiver, transceiving device, or networkinterface card (NIC).

Audio interface 238 is arranged to produce and receive audio signalssuch as the sound of a human voice. For example, audio interface 238 maybe coupled to a speaker and microphone (not shown) to enabletelecommunication with others and/or generate an audio acknowledgementfor some action.

Display 240 may be a liquid crystal display (LCD), gas plasma, lightemitting diode (LED), organic LED, or any other type of display usedwith a computing device. Display 240 may also include a touch sensitivescreen arranged to receive input from an object such as a stylus or adigit from a human hand.

Keypad 242 may comprise any input device arranged to receive input froma user. For example, keypad 242 may include a push button numeric dial,or a keyboard. Keypad 242 may also include command buttons that areassociated with selecting and sending images

Illuminator 244 may provide a status indication and/or provide light.Illuminator 244 may remain active for specific periods of time or inresponse to events. For example, when illuminator 244 is active, it maybacklight the buttons on keypad 242 and stay on while the client deviceis powered. Also, illuminator 244 may backlight these buttons in variouspatterns when particular actions are performed, such as dialing anotherclient device. Illuminator 244 may also cause light sources positionedwithin a transparent or translucent case of the client device toilluminate in response to actions.

Video interface 246 is arranged to capture video images, such as a stillphoto, a video segment, an infrared video, or the like. For example,video interface 246 may be coupled to a digital video camera, aweb-camera or the like. Video interface 246 may comprise a lens, animage sensor, and other electronics. Image sensors may include acomplementary metal-oxide-semiconductor (CMOS) integrated circuit,charge-coupled device (CCD), or any other integrated circuit for sensinglight.

Client device 200 also comprises input/output interface 248 forcommunicating with external devices, such as a headset, or other inputor output devices no shown in FIG. 2. Input/output interface 248 canutilize one or more communication technologies, such as USB, infrared,Bluetooth™, or the like.

Haptic interface 250 is arranged to provide tactile feedback to a userof the client device. For example, the haptic interface 250 may beemployed to vibrate client device 200 in a particular way when anotheruser of a computing device is calling. In some embodiments, hapticinterface 250 may be optional.

Client device 200 may also include GPS transceiver 232 to determine thephysical coordinates of client device 200 on the surface of the Earth.GPS transceiver 232, in some embodiments, may be optional. GPStransceiver 232 typically outputs a location as latitude and longitudevalues. However, GPS transceiver 232 can also employ othergeo-positioning mechanisms, including, but not limited to,triangulation, assisted GPS (AGPS), Enhanced Observed Time Difference(E-OTD), Cell Identifier (CI). Service Area Identifier (SAI), EnhancedTiming Advance (ETA), Base Station Subsystem (BSS), or the like, tofurther determine the physical location of client device 200 on thesurface of the Earth. It is understood that under different conditions.GPS transceiver 232 can determine a physical location within millimetersfor client device 200; and in other cases, the determined physicallocation may be less precise, such as within a meter or significantlygreater distances. In one embodiment, however, mobile device 200 maythrough other components, provide other information that may be employedto determine a physical location of the device, including for example, aMedia Access Control (MAC) address, IP address, or the like.

Mass memory 226 includes a Random Access Memory (RAM) 204, a Read-onlyMemory (ROM) 222, and other storage means. Mass memory 226 illustratesan example of computer readable storage media (devices) for storage ofinformation such as computer readable instructions, data structures,program modules or other data. Mass memory 226 stores a basicinput/output system (BIOS) 224 for controlling low-level operation ofclient device 200. The mass memory also stores an operating system 206for controlling the operation of client device 200. It will beappreciated that this component may include a general-purpose operatingsystem such as a version of UNIX, or LINUX™, or a specialized clientcommunication operating system such as Microsoft Corporation's WindowsMobile™, Apple Corporation's iOS™, Google Corporation's Android™ or theSymbian® operating system. The operating system may include, orinterface with a Java virtual machine module that enables control ofhardware components and/or operating system operations via Javaapplication programs.

Mass memory 226 further includes one or more data storage 208, which canbe utilized by client device 200 to store, among other things,applications 214 and/or other data. For example, data storage 208 mayalso be employed to store information that describes variouscapabilities of client device 200. The information may then be providedto another device based on any of a variety of events, including beingsent as part of a header during a communication, sent upon request, orthe like. Data storage 208 may also be employed to store socialnetworking information including address books, buddy lists, aliases,user profile information, or the like. Further, data storage 208 mayalso store message, webpage content, or any of a variety of usergenerated content. At least a portion of the information may also bestored on another component of network device 200, including, but notlimited to processor readable storage media 230, a disk drive or othercomputer readable storage devices (not shown) within client device 200.

Processor readable storage media 230 may include volatile, nonvolatile,removable, and non-removable media implemented in any method ortechnology for storage of information, such as computer- orprocessor-readable instructions, data structures, program modules, orother data. Examples of computer readable storage media include RAM.ROM. Electrically Erasable Programmable Read-only Memory (EEPROM), flashmemory or other memory technology, Compact Disc Read-only Memory(CD-ROM), digital versatile disks (DVD) or other optical storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other physical medium which can be usedto store the desired information and which can be accessed by acomputing device. Processor readable storage media 230 may also bereferred to herein as computer readable storage media and/or computerreadable storage device.

Applications 214 may include computer executable instructions which,when executed by client device 200, transmit, receive, and/or otherwiseprocess network data. Network data may include, but is not limited to,messages (e.g. SMS, Multimedia Message Service (MMS), instant message(IM), email, and/or other messages), audio, video, and enabletelecommunication with another user of another client device.Applications 214 may include, for example, browser 218, and otherapplications 220. Other applications 220 may include, but are notlimited to, calendar, search programs, email clients, IM applications,SMS applications, voice over Internet Protocol (VOIP) applications,contact managers, task managers, transcoders, database programs, wordprocessing programs, security applications, spreadsheet programs, games,search programs, and so forth.

Browser 218 may include virtually any application configured to receiveand display graphics, text, multimedia, messages, and the like,employing virtually any web based language. In one embodiment, thebrowser application is enabled to employ HDML, WML, WMLScript,JavaScript, SGML, HTML, XML, and the like, to display and send amessage. However, any of a variety of other web-based programminglanguages may be employed. In one embodiment, browser 218 may enable auser of client device 200 to communicate with another network device,such as SGSD 112, CSSD 114, LASD 116 of FIG. 1.

Illustrative Network Device

FIG. 3 shows one embodiment of a network device 300, according to oneembodiment of the invention. Network device 301) may include many moreor less components than those shown. The components shown, however, aresufficient to disclose an illustrative embodiment for practicing theinvention. Network device 300 may be configured to operate as a server,client, peer, a host, or any other device. Network device 300 mayrepresent, for example SGSD 112, CSSD 114, LASD 116 of FIG. 1, and/orother network devices.

Network device 300 includes processor 302, processor readable storagemedia 328, network interface unit 330, an input/output interface 332,hard disk drive 334, video display adapter 336, and memory 326, all incommunication with each other via bus 338. In some embodiments,processor 302 may include one or mote central processing units.

As illustrated in FIG. 3, network device 300 also can communicate withthe Internet, or some other communications network, via networkinterface unit 330, which is constructed for use with variouscommunication protocols including the TCP/IP protocol. Network interfaceunit 330 is sometimes known as a transceiver, transceiving device, ornetwork interface card (NIC).

Network device 300 also comprises input/output interlace 332 forcommunicating with external devices, such as a keyboard, or other inputor output devices not shown in FIG. 3. Input/output interlace 332 canutilize one or more communication technologies, such as USB, infrared.Bluetooth™, or the like.

Memory 326 generally includes RAM 304, ROM 322 and one or more permanentmass storage devices, such as hard disk drive 334, tape drive, opticaldrive, and/or floppy disk drive. Memory 326 stores operating system 306for controlling the operation of network device 300. Any general-purposeoperating system may be employed. Basic input/output system (BIOS) 324is also provided for controlling the low-level operation of networkdevice 300.

Although illustrated separately, memory 326 may include processorreadable storage media 328. Processor readable storage media 328 may bereferred to and/or include computer readable media, computer readablestorage media, and/or processor readable storage device. Processorreadable storage media 328 may include volatile, nonvolatile, removable,and non-removable media implemented in any method or technology forstorage of information, such as computer readable instructions, datastructures, program modules, or other data. Examples of processorreadable storage media include RAM, ROM. EEPROM, flash memory or othermemory technology. CD-ROM, digital versatile disks (DVD) or otheroptical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other media which canbe used to store the desired information and which can be accessed by acomputing device.

Memory 326 further includes one or more data storage 308, which can beutilized by network device 300) to store, among other things,applications 314 and/or other data. For example, data storage 308 mayalso be employed to store information that describes variouscapabilities of network device 300. The information may then be providedto another device based on any of a variety of events, including beingsent as part of a header during a communication, sent upon request, orthe like. Data storage 308 may also be employed to store messages, webpage content, or the like. At least a portion of the information mayalso be stored on another component of network device 300, including,but not limited to processor readable storage media 328, hard disk drive334, or other computer readable storage medias (not shown) within clientdevice 300.

Data storage 308 may include a database, text, spreadsheet, folder,file, or the like, that may be configured to maintain and store useraccount identifiers, user profiles, email addresses, IM addresses,and/or other network addresses; or the like. Data storage 308 mayfurther include program code, data, algorithms, and the like, for use bya processor, such as processor 3102 to execute and perform actions. Inat least one embodiment data storage 308 may include at least one statetable to indicate which tags are in a particular state. In oneembodiment, at least some of data store 308 might also be stored onanother component of network device 300, including, but not limited toprocessor-readable storage media 328, hard disk drive 334, or the like.

Applications 314 may include computer executable instructions, which maybe loaded into mass memory and run on operating system 306. Examples ofapplication programs may include transcoders, schedulers, calendarsdatabase programs, word processing programs, Hypertext Transfer Protocol(HTTP) programs, customizable user interface programs. IPSecapplications, encryption programs, security programs, SMS messageservers, IM message servers, email servers, account managers, and soforth. Applications 314 may also include website server 318, LoggingAgent Application (LAA) 319, Score Generator Application (SGA) 320, andContent Selection Application (CSA) 321.

Website server 318 may represents any of a variety of information andservices that are configured to provide content, including messages,over a network to another computing device. Thus, website server 318 caninclude, for example, a web server, a File Transfer Protocol (FTP)server, a database server, a content server, or the like. Website server318 may provide the content including messages over the network usingany of a variety of formats including, but not limited to WAP, HDML,WML, SGML, HTML, XML, Compact HTML (cHTML), Extensible HTML (xHTML), orthe like.

LAA 319 may be configured to collect and/or store data associated with aplurality of users, as described above in conjunction with LASD 116 ofFIG. 1. In some embodiments, LAA 319 may be employed by LASD 166 ofFIG. 1. In any event, LAA 319 may employ processes, or parts ofprocesses, similar to those described in conjunction with FIGS. 4-5, toperform at least some of its actions.

SGA 320 may be configured to determine a recommendation score of contentfor a user, as described above in conjunction with SGSD 112 of FIG. 1.In some embodiments, SGA 320 may be employed by SGSD 112 of FIG. 1. Inany event. SGA 320 may employ processes, or parts of processes, similarto those described in conjunction with FIGS. 4-5, to perform at leastsome of its actions.

CSA 321 may be configured to select content to provide to a user basedon a recommendation score of the content, as described above inconjunction with CSSD 114 of FIG. 1. In some embodiments, CSA 321 may beemployed by CSSD 114 of FIG. 1. In any event, CSA 321 may employprocesses, or parts of processes, similar to those described inconjunction with FIGS. 4-5, to perform at least some of its actions.

General Operation

The operation of certain aspects of the invention will now be describedwith respect to FIGS. 4-6. FIG. 4 illustrates a logical flow diagramgenerally showing one embodiment of an overview process for providingcontent to a user based on a recommendation score of the content for theuser. In some embodiments, process 400 of FIG. 4 may be implemented byand/or executed on a single network device, such as network device 300of FIG. 3. In other embodiments, process 400 or portions of process 400of FIG. 4 may be implemented by and/or executed on a plurality ofnetwork devices, such as network device 3001 of FIG. 3.

Process 400 begins, after a start block, at block 402, where a piece ofcontent may be selected. In at least one embodiment, the content may beselected by a publisher, editor, or the like. In some embodiments, thecontent may be selected from a plurality of different possible contentthat may be provided to a user (also referred to herein as a reader).For example, a plurality of possible content may include, but is notlimited to, “today's top stories,” content regarding breaking news, mostpopular content among readers, editorially-curated content of specialimportance, randomly selected content, or the like. In some embodiments,process 400 may be employed for each of the plurality of possiblecontent that may be provided to a user. Accordingly, a subset of theplurality of possible content may be provided to the user based on acalculated recommendation score for each piece of content (as describedin more detail below).

Process 400 continues at block 404, where an intrinsic value of thecontent may be determined. In at least one embodiment, the intrinsicvalue may be determined by employing one or more statistical analysistools on historical data associated with the user. In at least oneembodiment the historical data may include previous content provided tothe user and actions taken by the user on the previous content. Suchactions may include, hut are not limited to, clicking on the previouscontent, sharing the previous content, “liking” the previous content,how long the user accessed the previous content, or the like.

In some embodiments, the intrinsic value of the selected content may becalculated based on a type of action performed by the user on previouscontent that is similar to the selected content. In other embodiments,the intrinsic value of the selected content may be calculated based onnumber of times the user accessed previous content similar to theselected content. In yet other embodiments, the intrinsic value of theselected content may be calculated based on a time spent by the useraccessing previous content similar to the selected content. However,embodiments are not so limited and other methods and/or algorithms maybe employed to determine the intrinsic value of the content to the user.Content similar to selected content may be based on keywords within thecontent, a category of the content (e.g., sports, politics, or thelike), type of content (e.g., news article, video, blog post, or thelike), an author and/or publisher of the content, or the like, or anycombination thereof.

In some other embodiments, the intrinsic value may be selected by theuser, such that the intrinsic value of the selected content may be basedon a category of the content and the user selected intrinsic value forthat category. In at least one such embodiment, the user may be enabledto determine and/or select intrinsic values for different categories ofcontent. For example, the user may select an intrinsic value of ‘1’ forcontent categorized us political (i.e., the user is not interested inpolitical related content) and a ‘9’ for content categorized as sports(i.e., the user is very interested in sports related content). In otherembodiments, a user may be enabled to select intrinsic values forsubcategories. For example, the category of sports may includesubcategories of baseball and football. In this example, the user mayselect an intrinsic value of ‘2’ for baseball (i.e., the user is notinterested in baseball related content) and ‘9.3’ for football (i.e.,the user is very interested in football related content). However,embodiments are not so, limited and other algorithms and/or metrics maybe employed to determine an intrinsic value of the selected content tothe user.

Process 400 proceeds next to block 406, which is described in moredetail below in conjunction with FIG. 5. Briefly, however, at block 406,a social value of the content may be determined. In at least oneembodiment, the social value may be determined based on a combination ofan individual social value of the selected content for each of aplurality of friends of the user.

Process 400 continues next at block 408, where a recommendation score ofthe content for the user may be calculated. In at least one embodiment,the recommendation score may be calculated based on a combination of theintrinsic value and the social value. In some embodiments, the intrinsicvalue may be added to the social value. However, embodiments are not solimited and other methods and/or algorithms may be employed forcombining the intrinsic value and the social value. A use caseillustration of an equation that may be employed to calculate therecommendation score of the selected content for the user is describedin more detail below.

In at least one of various embodiment, an intrinsic weight may beapplied to the intrinsic value. In at least one embodiment, theintrinsic weight may be multiplied by the intrinsic value. In someembodiments, employing an intrinsic weight may indicate how much theuser may value the personal relevance of the selected content (i.e., theintrinsic value compared to the social relevance of the selected content(i.e., the social value). For example, in some embodiments, theintrinsic weight may be increased if it is more important to the user tobe provided articles related to the user's personal interests ratherthan articles related to the interests of the user's social network.However, in other embodiments, the intrinsic weight may be decreased ifit is more important to the user to be provided articles related to theinterest of the user's social network rather than articles related tothe user's personal interests.

The intrinsic weight may be static or dynamic. In at least oneembodiment, an initial intrinsic weight may be determined, such as, forexample, by an operator, publisher, or the like. In one non-limiting,non-exhaustive example, the initial intrinsic weight may be determinedsuch that the intrinsic value and the social value are equally importantto the user. In some embodiments, the intrinsic weight may change overtime based on how the user interacts with content. For example, if theuser continuously clicks on content that has a higher social value thanintrinsic value, then the social value may be more important to theuser, which may result in a decrease in the intrinsic weight. However,if the user continuously clicks on content that has a higher intrinsicvalue than social value, then the intrinsic value may be more importantto the user, which may result in an increase in the intrinsic weight.

In some other embodiments, a social weight may be applied to the socialvalue. In at least one such embodiment, the social weight may beemployed in a manner similar to the intrinsic weight. For example, thesocial weight may be multiplied by the social value.

In any event, process 400 continues at block 410, where the content maybe provided to the user. In some embodiments, providing content to theuser may include displaying the content to the user in a personalizedcontent stream on a webpage visited by the user, the user's home page,or the like. In at least one embodiment, the content may be providedbased on the calculated recommendation score. For example, thecalculated recommendation score may be employed to determine whether ornot to provide the selected content to the user an order of providingthe selected content along with other content to the user, or the like.

As described above, process 400 may be employed for a plurality ofpossible content to be provided to the user. In some embodiments, arecommendation score (as calculated at block 408) of each piece ofpossible content may be utilized to determine which content to provideto the user. In other embodiments, the calculated recommendation scoreof each piece of content may be ranked for the user. The content may beordered and provided to the user based on the ranking of recommendationscores. For example, content with the top x number of recommendationscores (e.g., the top five recommendation scores may be ordered andprovided to the user.

In another embodiment, content with a recommendation score above athreshold value may be provided to the user. The threshold value may bedetermined by a user and/or an operator. In some embodiments, thethreshold value may change over time based on actions by the user oncontent provided to the user. For example, if the user typicallyaccesses content with a recommendation score above a particular value,then this value may be determined to be the threshold value.

In some other embodiments, content with a recommendation score that doesnot meet the requirements for being provided to the user (e.g., not atthe top of the ranking and/or not above the recommendation scorethreshold value) may still be provided to the user. For example, 10-30%of the remaining content that did not make the cut may be shown to theuser (e.g., in the personalized content stream). Various heuristics maybe employed to determine which of the remaining content to provide tothe user, including, but not limited to, content representing breakingnews, editorially-curated content of special importance, most popularcontent, randomly selected content, or the like, or any combinationthereof.

In at least one embodiment, by providing this additional content to theuser, additional data may be collected and/or analyzed to dynamicallymodify how the recommendation score of content is calculated (e.g.,modifying the intrinsic weight applied to the intrinsic value, modifyingan interest probability (P(F_(b)X, U), as described below), modifying aweight of a friend (a_(i) as described below), or the like).

After block 410, process 400 may return to a calling process to performother actions.

Ideas described herein are applicable not only to online publishing; butvirtually any realm where social feedback loops are involved—and aremeasurable—may benefit from employing embodiments of process 400, asdescribed above. Some non-limiting, non-exhaustive examples include, butare not limited to online gaming, e-commerce, advertisement targeting,or the like.

In some embodiments, in multi-player online role playing games, a usermight be suggested a particular path of character upgrades because hisguild members appreciate this kind of support from their friends (whererecommendations scores of character upgrades may be calculated similarto calculating recommendations scores of content as described above).For example, the intrinsic value may be a gamer's personal interest inplaying a particular character, using particular skills or attributes,using particular weapons, or the like. The social value may be theinterest that the gainer's friends (e.g., his guild members) have for—orthe interest the gamer's friends have in the gainer using—the particularcharacter, skills, upgrades, attributes, or the like. A recommendationscore may then be determined (by employing embodiments described herein)for each of a plurality of different characters, skills, upgrades, orthe like, to suggest and/or influence the gamer's character selections.These suggestions may enable gamers to create and play characters thatmay be most beneficial to then friends/guild as a whole.

In other embodiments, in an online shopping site that maintains a useridentity, a user might be suggested a set of purchases (i.e., content)that their friends are most likely to value, based upon their priorpurchases. For example, if a user's friend bought a last years' model ofa product, it is likely going to be fruitful to suggest this years'model of this product to the user. In at least one example, theintrinsic value may be a purchaser's interest in a particular product,line of products, supplier, brand, or the like. The social value may bethe interest that the purchaser's friends have in the particularproduct, line of products, supplier, brand, or the like. Arecommendation score may then be determined (by employing embodimentsdescribed herein) for each of a plurality of different products, tosuggest products to purchase (or provide advertisements for) such thatthe purchaser looks good to her friends.

By employing embodiments described above, in some other embodiments,advertisements (i.e., content) may be determined to be provided to auser. In some embodiments, those advertisements (or other content) maybe provided and/or sold to one or more publishers. In at least oneembodiment, a publisher may be enabled to opt out from having datacollected about its users and used to generate recommendation scores ofcontent for another publisher to provide to the other publisher's users.

FIG. 5 illustrates a logical flow diagram generally showing oneembodiment of a process for determining a social value of content to auser. In some embodiments, process 500 of FIG. 5 may be implemented byand/or executed on a single network device, such as network device 300of FIG. 3. In other embodiments, process 500 or portions of process 500of FIG. 5 may be implemented by and/or executed on a plurality ofnetwork devices, such as network device 300 of FIG. 3.

Process 500 begins, after a start block, at block 502, where a friend ofthe user ma) be selected. In at least one of the various embodiments,the friend may be a first-degree friend of the user's social network. Insome embodiments, the friend may be selected based on a contact list ofthe user, other users that the user has been in contact with (e.g.,send/receive emails, text messages, or other person-to-person onlinecommunications, sharing content, or the like), or the like.

Process 500 proceeds to block 504, where a recommendation score ofcontent (i.e., the selected content at block 402 of FIG. 4) for theselected friend may be determined. In at least one embodiment, therecommendation score of the selected content for the selected friend maybe determined by recursively employing blocks 404, 406, 406, and 408 ofFIG. 4 for the selected friend. Accordingly, calculating therecommendation score of the selected content for the user, as describedby blocks 404, 406, and 408 of FIG. 4, may be determined by traversingone or more levels of the social network tree of the user.

In some embodiments, recommendation score for the selected friend may berecursively determined up to a predetermined degree of friends of theuser. For example, recommendations score may be recursively determinedop to third-degree friends of the user. In at least one embodiment, therecommendation score for a friend at the predetermined degree of friendsmay be determined based on the intrinsic value (as calculated by block404 of FIG. 4) of the content for those friends and not the social value(as calculated by block 406 of FIG. 4). For example, if thepredetermined degree of friends is first-degree friends of the user,then the recommendation score of the content for the selected friend(i.e., first-degree friend) may be an intrinsic value of the content tothe selected friend. In some embodiments, ending this recursivecalculation may be referred to a trimming the social network tree of theuser.

In any event, process 500 next proceeds to block 506, where an interestprobability for the selected friend may be determined. In at least oneof various embodiments, the interest probability may be a probabilitythat the user and the selected friend will perform an action on theselected content. In some embodiments, the interest probability may bebased on the selected friend performing an action on a piece of contentafter the user performs an action on the same piece of content. In otherembodiments, the interest probability may be based on the userperforming an action on a piece of content after the selected friendperforms an action on the same piece of content. Actions performed onthe content may include, but are not limited to, accessing the content,sharing the content, or the like.

In at least one embodiment, the interest probability for the selectedfriend may be determined by employing one or more statistical tools onhistorical data associated with the user and the selected fend. However,embodiments are not so limited and other methods and/or algorithms maybe employed to determine the interest probability for the selectedfriend for the selected content. In some embodiments, the historicaldata may include actions performed by the user and the selected friend,including which content was read by the user, which friends accessed thecontent (e.g., if the user shared the content with the friends), or thelike. In some embodiments, a unique link may be generated to sharecontent from the user to a friend, which may be utilized to determinewhich friends accessed the content.

In other embodiments, the historical data may identity actions performedon different features of the content, including but not limited to,given pieces of content, types of content (e.g., videos, articles, orthe like), categories of content (e.g., sports, health, or the like),authors of content, or the like. In some embodiments, the features maybe determined based on a natural language search of content, tags ofcontent, collaborative filters, or the like. In some embodiments, thehistorical data may indicate who performed an action on the contentfirst, the timeframe between actions, or the like. In some embodiments,the historical data may be utilized to generate a feature to friendmatrix, which may determine the interest probability for the selectedcontent for the selected friend.

In some embodiments, the interest probability may be unique fordifferent content features (e.g., types of content, categories ofcontent, authors of content, or the like) for the selected friend. Inother embodiments, the interest probability for the selected friend maybe an average of these different feature probabilities. However,embodiments are not so limited and other methods and/or algorithms maybe employed to determine the interest probability.

In any event, the interest probability for the selected friend may bestatic or dynamic. In at least one embodiment, the interest probabilityof the content and the selected friend may change over time based onactions by the friend and the user, for example, if the user tends toread the same sports articles that Friend 1 reads, then the interestprobability may increase for sports articles.

Process 500 continues at block 508, where a weight (i.e., a socialweight) of the selected friend may be determined. In at least oneembodiment, the weight may indicate how influential the selected fiendis to the user. In another embodiment, the weight may indicate bow muchthe user values and/or appreciates the selected friend's' opinion. Insome embodiments, each friend of the user (i.e., first-degree friend)may be associated with a unique weight.

In some other embodiments, the weight may indicate how influential theuser is on the selected friend. In at least one such embodiment, apublisher may be able to indirectly attract additional readers byemploying an increased weight if a user is very influential on a givenfriend, for example, if the publisher knows that the user is veryinfluential on the selected friend, then there may be an increasedlikelihood that the user the will tell the friend about content the useraccessed, which may result in the friend later accessing the samecontent.

The weight of the selected friend may be static or dynamic. In at leastone embodiment, the weight for each friend may be determined, such as,for example, by an operator, publisher, or the like. In onenon-limiting, non-exhaustive example, the initial weight may bedetermined such that each friend is weighted as equally important to theuser. In at least one embodiment, the weight of a friend may change(increase and/or decrease) over time based on actions by the friend andthe user. For example, if the user tends to read the same articles thatFriend 1 reads, then Friend 1 may be very influential to the user, whichmay result in an increase in the weight. In some other embodiments, theuser may be enabled to select the weight of a friend. For example, theuser may be enabled to indicate that Friend 2 is more influential thanFriend 1, but less influential than Friend 3, and so on.

Process 500 proceeds to block 510, where the individual social value ofthe content for the selected friend may be calculated. In at least oneof various embodiments, the individual social value for the selectedfriend may be calculated based on a combination of the weight (asdetermined at block 508), the recommendation score for the selectedfriend (as determined at block 504), and the interest probability forthe selected friend (as determined at block 506). In some embodiments,the weight, recommendation score, and interest probability may bemultiplied together.

In some embodiments, the individual social value may be an indication ofthe overall perception of the user within the user's social network. Forexample, in some situations, a user may appear smarter to his/herfriends (i.e., the user's social network) if the user reads an articlebefore the friends do and the article is of interest to the friends. Inthis situation, if a friend later reads the article, then the interestprobability for the friends may increase for similar content, which mayincrease the individual social value of similar content for the friend.In other situations, a user may be more engaged in a discussion withhis/her friends if the user reads an article that was also read by thefriends. In both of these situations, the interest probability forfriends that also access the content may increase for similar content,which may increase the individual social value of similar content forthe friend.

In any event, process 500 continues at decision block 512, where adetermination may be made whether to select another friend of the user.In some embodiments, another friend may be selected until all firstdegree friends of the user have been selected. In at least one ofvarious embodiments, a user's friends may be grouped into subsets offriends, such as, but not limited to, school friends, online socialfriends, work friends, family members, or the like. In one suchembodiment, another friend may be selected until a subset of thefirst-degree friends of the user has been selected. If another friendmay be selected, then process 500 may loop to block 502 to selectanother friend; otherwise, process 500 may flow to block 514.

At block 514, the individual social value for each friend may becombined to calculate the social value of the content to the user. Insome embodiments, the social value may be the sum of the individualsocial value of each first-degree friend of the user.

After block 514, process 500 may return to a calling process to performother actions. FIG. 6 illustrates a logical flow diagram generallyshowing one embodiment of a process for determining content to provideto a user. In some embodiments, process 600 of FIG. 6 may be implementedby and/or executed on a single network device, such as network device300 of FIG. 3. In other embodiments, process 600 or portions of process600 of FIG. 6 may be implemented by and/or executed on a plurality ofnetwork devices, such as network device 300 of FIG. 3.

Process 600 begins, after a start block, at block 602, where a pluralityof possible content may be determined. In some embodiments, theplurality of possible content may be selected by a publisher, editor, orthe like. In at least one embodiment, the possible content may includecontent that can be provided to a user at a particular time, for aparticular day, when a user accesses a channel, or the like. Examples ofthe plurality of possible content may include, but are not limited to,“today's top stories,” content regarding breaking news, most popularcontent among readers editorially-curated content of special importance,randomly selected content, or the like.

In at least one of various embodiments, the plurality of possiblecontent may include content associated with a plurality of differentcategories, such as, for example, sports, politics, economics, and thelike. In at least one such embodiment, each category may includesub-categories. For example, the sports category may includesub-categories for different types of sports (e.g., tennis, basketball,football, or the like).

Process proceeds to block 604, where one or more social networksassociated with the user may be identified. These social networks mayinclude, but are not limited to, family network, work network, collegenetwork, close personal friend network, or the like. In someembodiments, these different social networks may be determined by theuser (e.g., if the user identifies which friends are members of whichsocial network).

In other embodiments, the different social networks may be automaticallydetermined based on clusters of friends. For example, various clusteringalgorithms may be employed to determine one or more social networksbased on a correlation of friends and friends of friends (e.g., users A,B and C may be clustered into a same social network if user A is friendswith users B and C, and user B is friends with user C. However,embodiments are not so limited, and other algorithms and/or mechanismmay be employed to determine and/or identify the users one or moresocial networks. It should be recognized that friends of the user may bemembers of more than one social network of the user.

Process 600 continues at decision block 606, where one of the identifiedsocial networks may be selected. In some embodiments, each differentsocial network may be separately selected, such that each social networkassociated with the user may be selected once. If an identified socialnetwork is selected, then process 600 may flow to decision block 608 forthe selected social network; otherwise, process 600 may flow to block614.

At decision block 608, possible content may be selected. In someembodiments, each of the plurality of possible content may be separatelyselected, such that each piece of content may be selected once for eachseparate social network. If a possible content is selected, then process600 may flow to block 610; otherwise, process 600 may flow to block 612.

At block 610, a recommendation scone of the selected content may bedetermined for the user for the selected social network. In at least oneof various embodiments, block 610 may employ embodiments of blocks 404,406, and 408 of FIG. 4 to determine the recommendation score of theselected content. Since each piece of content may be selected for eachsocial network, each piece of possible content may have a separaterecommendation score determined for each corresponding social network.After block 610, process 600 may loop to decision block 608 to selectanother piece of possible content.

If, at decision block 608, another piece of possible content is notselected, then process 600 may flow from decision block 608 to block612. At block 612, the plurality of possible content may be rank orderedbased on the determined recommendation scores for the selected socialnetwork. After block 612, process 600 may loop to decision block 606 toselect another identified social network.

If, at decision block 606, another social network of the user is notselected, then process 600 may proceed from decision block 606 to block614. At block 614, a portion of the possible content may be provided tothe user. In various embodiments, one or more pieces of ranked contentfor each social network may be provided to the user. In someembodiments, a predetermined number of top ranked possible content foreach social network (e.g., top two ranked pieces of content for eachsocial network) may be provided to the user. For example, the user maybe provided two pieces of content with a highest rank for their worknetwork, two, other pieces of content with a highest rank for theirfamily network, and so on. Since various social networks may havedifferent social interests, such embodiments may allow a user to viewcontent that is most valuable and/or applicable to the interests (e.g.,highest ranking) of each of the different social networks of the user.

In other embodiments, the number of pieces of content to provide to theuser for each social network may be determined based on a user'sinterests and/or weights for each social network. In at least oneembodiment, these social network weights may be determined by the user.For example, the user may indicate that he would like to receive fourhighest-ranked pieces of content for his work network and twohighest-ranked pieces of content for his family network. In at least onesuch embodiment, the user may assign a higher social network weight tohis work network than to his family network.

In another embodiment, the social network weights may be determinedbased on actions performed by the user. For example, if the user viewsmore content associated with his close personal friend network, than hiswork network, then the close personal friend network may be assigned ahigher weight than their work network.

However, embodiments are not so limited and other mechanisms and/oralgorithms may be employed to determine which of the ranked content foreach social network may be provided to the user. For example, in otherembodiments, content that is ranked above a predetermined thresholdranking (e.g., top five ranking) for a plurality of different socialnetworks may be provided to the user. Such an embodiment may providecontent to the user that is applicable to multiple social networks ofthe user. The various embodiments described herein may enable a user toreceive more content that is relevant to specific social networks thanother social networks, but may still receive content that is relevant toat least some of the other social networks.

After block 614, process 600 may return to a calling process to performother actions.

It will be understood that each block of the flowchart illustration, andcombinations of blocks in the flowchart illustration, can be implementedby computer program instructions. These program instructions may beprovided to a processor to produce a machine, such that theinstructions, which execute on the processor, create mans forimplementing the actions specified in the flowchart block or blocks. Thecomputer program instructions may be executed by a processor to cause aseries of operational steps to be performed by the processor to producea computer-implemented process such that the instructions, which executeon the processor to provide steps for implementing the actions specifiedin the flowchart block or blocks. The computer program instructions mayalso cause at least some of the operational steps shown in the blocks ofthe flowchart to be performed in parallel. Moreover, some of the stepsmay also be performed across more than one processor, such as mightarise in a multi-processor computer system. In addition, one or moreblocks or combinations of blocks in the flowchart illustration may alsobe performed concurrently with other blocks or combinations of blocks,or even in a different sequence than illustrated without departing fromthe scope or spirit of the invention.

Accordingly, blocks of the flowchart illustration support combinationsof means for performing the specified actions, combinations of steps forperforming the specified actions and program instruction means forperforming the specified actions. It will also be understood that eachblock of the flowchart illustration, and combinations of blocks in theflowchart illustration, can be implemented by special purposehardware-based systems, which perform the specified actions or steps, orcombinations of special purpose hardware and computer instructions. Theforegoing example should not be construed as limiting and/or exhaustive,but rather, an illustrative use case to show an implementation of atleast one of the various embodiments of the invention.

Illustrative Use Case Recommendation Score Algorithm

As described above, a recommendation score (which may also be referredto as a Social Value Recommendation Score) may be determined for a givenpiece of content for a particular user. An embodiment for determiningthe Social Value Recommendation Score may be recursively defined as:

${S\left( {X,U} \right)} = {{a_{0}*{N\left( {X,U} \right)}} + {\sum\limits_{i = 1}^{n}\left( {a_{i}*{P\left( {F_{i},X,U} \right)}*{S\left( {X,F_{i}} \right)}} \right)}}$

where, each component of the equation is described in more detail below.

X may refer to a given piece of content. In some embodiments, content Xmay be content that may be provided to user U, such as content displayedon a homepage (or other webpage) of user U.

U may refer to a particular user that may be enabled to access content.In some embodiments, the S(X, U), as described above, may be determinedfor content X when user U accesses a webpage. In some embodiments user Umay be identified when the user logs into a webpage, based on a cookieor other unique identifier, or the like.

S(X, U) may be the Social Value Recommendation Score assigned to contentX for user U. Embodiments may be employed to maximize S(X, U) withrespect to parameters a_(i) and/or a₀ using a known training set ofdata. In at least one embodiment, the training set of data may includecontent (e.g., content X) and/or a social graph of users. In someembodiments, the social graph of users may include friends of user U,which may include first degree friends, second degree friends, or thelike. In various embodiments, the overall score may be a function thatdetermines how to combine scores (S(X, U)) of each of the user's friendsby employing weights a_(i) and the intrinsic value (N(X, U) to the user.

N(X, U) may be the intrinsic value that user U obtains from content X.In at least one embodiment, N(X, U) may be calculated by employingembodiments of block 404 of FIG. 4. In some embodiments, the intrinsicvalue may be calculated based on a time spent by user U, accessingcontent similar to content X. In other embodiments, the intrinsic valuemay be calculated based on a number of times user U may share contentsimilar to content X. In some embodiments content similar to content maybe based on keywords within the content, a category of the content(e.g., sports, politics, or the like), type of content (e.g., newsarticle, video, blog post, or the like), or the like, or any combinationthereof.

a₀ may be the weigh of the intrinsic value for user U. In someembodiments, a user may be impacted greater by the intrinsic value (N(X,U)) (i.e., the intrinsic value may be more important to the user) thanthe social value and thus, may have a higher value of to, than a userwho may be impacted more by the social perception (i.e., the socialvalue) than the intrinsic value.

n may be the number of friends that user U has. In at least oneembodiment, n may be the number of first-degree friends in the socialnetwork of user U.

F_(i) may be each friend of user U. In at least one embodiment, F, maybe each first degree friend of user U.

Σ_(i=1) ^(n)(a_(i)*P(F_(i), X, U)*S(X, F_(i))) may be the social valueof friends F_(i) of user U for content. In at least one embodiment, thissocial value may be calculated by employing embodiments of block 406 ofFIG. 4. In some embodiments, a_(i)*P(F_(i),X, U)*S(X,F_(i)) may bereferred to as the individual social value of content X for friendF_(i).

S(X,F_(i)) may be the Social Value Recommendation Score assigned tocontent X for friend F_(i). In at least one embodiment, S(X,F_(i)) maybe calculated by employing embodiments of block 504 of FIG. 4, which mayinclude employing embodiments of blocks 404, 406, and 408 of FIG. 4,where F_(i) is the user (i.e., user U in the recommendation scoreequation above).

P(F_(i),X, U) may be the probability that user U will be interested inand/or will access content X after friend F_(i) accesses content X. Inat least one embodiment, this probability may be calculated by employingembodiments of block 506 of FIG. 5. In some embodiments, the probabilitymay be determined from mining historical behavior of user U in responseto actions of friend F_(i), combined with factor analysis of contentsimilar to content X.

a_(i) may be the weight of how influential friend F_(i) is to user U. Inat least one embodiment, a_(i) may be calculated by employingembodiments of block 508 of FIG. 5. In some embodiments, one or more offriends F_(i) may be more influential to user U, which may have moreimpact on the overall score (S(X, U)), than less influential friends.For example, a friend that is closer to user U in the offline world maybe more influential and have a higher weight a_(i), than an online-onlyfriend.

In some embodiments. S(X,F_(i)) may be specified using a linearcombination of weights, such as described above. In other embodiments,S(X,F_(i)) may be based on and/or include second and higher ordereffects into the intrinsic value placed by the user's friends. At leastone such embodiment may model the fact that if several of friends F_(i)are interested in topic T, then there may be a ion-linear return oninvestment for user U to become knowledgeable and/or share content Xthat covers topic T.

As described above, S(X,F_(i)) may be recursively calculated. In someembodiments, the function may be recursively performed a predeterminednumber of times (i.e., prune the tree of social value after a certainnumber of recursive steps away from the original user). In at least oneembodiment, the number of time S(X,F_(i)) may recursively be performedmay be determine experimentally for a given social graph, by performinga statistical evaluation of a given social graph, or the like. Forexample, in one embodiment, a contribution of each time S(X,F_(i)) isrecursively performed may be calculated. S(X,F_(i)) may continue to berecursively performed until the calculated contribution is less than athreshold value. In some embodiments, the more recursive stepsperformed, the more computationally expensive the algorithm may become.In at least one embodiment, probabilities P(F_(i),X, U) and socialweights at may be values bounded by 0 and 1. In such an embodiment, eachrecursive step away from user U may provide less and less incrementalvalue to S(X,F_(i)), which may result in S(X,F_(i)) converging on aparticular value (e.g., when the calculated contribution is less than adefined threshold value).

Illustrative Use Case Social Network

FIG. 7 illustrates a use case embodiment of a social network of a userthat may be utilized to determine a recommendation score for a user.Social network 700 may be a tree of friends of user 702. Social network700 may include user 702, first-degree friends 726 and second-degreefriends 726. First-degree friends 726 may be first-degree friends ofuser 702 and may include friends 704-707. Second-degree friends 728 maybe second-degree friends of user 702. Although only two degrees offriends are illustrated, additional degrees of friends may also beemployed.

As described above, a recommendation score of content for user 702 maybe calculated based on a combination of an intrinsic value and a socialvalue. The intrinsic value for user 702 may include intrinsic value 703,which may be an embodiment of N(X, U), as described above. The socialvalue for user 702 way be calculate based on a combination of anindividual social value for each of the user's friends (friends704-707). The individual social value hr each friend man be determinedbased on a combination of a social weight, an interest probability, anda recommendation score. As illustrated, each friend may be associatedwith a unique social weight, interest probability, and/or recommendationscore for the given content.

For example, the individual social value for friend 704 may becalculated based on a combination of weight 710, interest probability712, and recommendation score 714. Weight 710 may be an embodiment ofa_(i), as described above. Interest probability may be an embodiment ofP(F_(i),X, U), as described above. Recommendation score 714 may be anembodiment of S(X, F_(i)), as described above.

In some embodiments, recommendation score 714 may be determined forfriend 704 by combining intrinsic value 715 for friend 704 (e.g., asdetermined at block 404 of FIG. 4) and a social value of the content tofriend 704 (e.g., as determined at block 406 of FIG. 4). As describedabove, the social value for user 704 may be calculated based on acombination of an individual social value for each friend of friend 704(including friend 722). The individual social value for friend 722 maybe determined based on a combination of weight 716, interest probability718, and recommendation score 720. Although FIG. 7 illustrates ellipsesto indicate additional degrees of separation from user 702, embodimentsmay be constrained to a predetermined degree of separation. If therecursive function for determining the recommendation score for user 702is stopped at second-degree friends 728, then recommendation score 720may be based on intrinsic value 723 for friend 722.

The above specification, examples, and data provide a completedescription of the composition, manufacture, and use of the invention.Since many embodiments of the invention can be made without departingfrom the spirit and scope of the invention, the invention resides in theclaims hereinafter appended.

1. A method for managing a plurality of content for display to a user,wherein at least one network computer enables actions to be performed,comprising: identifying at least one social network associated with theuser; and for each of the at least one social network, determining arecommendation score for each of a plurality of content for the user,wherein the recommendation score for a content is based on at least acombination of individual recommendation scores for the content for eachfriend in an identified social network, wherein the individualrecommendation score is recursively determined to a predetermined degreeof separation from the user in the identified social network; andproviding a subset of the plurality of content to the user based on therecommendation score of each of the plurality of content for each of theat least one social network.
 2. The method of claim 1, wherein providingthe subset of the plurality of content to the user further comprises:ranking the plurality of content separately for each of the at least onesocial network based on the recommendation scores of each of theplurality of content; and providing a predetermined number of highestranking content for each social channel to the user.
 3. The method ofclaim 1, further comprising determining the subset of the plurality ofcontent based on a weight of each of the at least one social network andthe recommendations scores for the plurality of content for each of theat least one social network.
 4. The method of claim 1, wherein therecommendation score of content for a social network is based on acombination of an intrinsic value of the content to the user and asocial value of the content that is based on the combination of theindividual recommendation scores for each friend in the social network.5. The method of claim 1, wherein determining the recommendation scoreof content for a social network further comprises, weighting at least anintrinsic value based on an importance to the user of the user'spersonal interests in comparison to social interests of the socialnetwork.
 6. The method of claim 1, further comprising: determining anintrinsic value of each of the plurality of content based on at leastone action that was previously taken by the user on other content thatis similar to corresponding content; and employing the intrinsic valueto determine the recommendation score.
 7. The method of claim 1, whereinthe individual recommendation score for each respective friend isweighted based on at least an influence of the respective friend on theuser.
 8. The method of claim 1, wherein the individual recommendationscore for each respective friend is combined with an interestprobability that both the respective friend and the user perform anaction on content.
 9. A network computer for managing a plurality ofcontent for display to a user, comprising: a memory for storinginstructions; and a processor that executes the instructions to enableactions, including: identifying at least one social network associatedwith the user; and for each of the at least one social network,determining a recommendation score for each of a plurality of contentfor the user, wherein the recommendation score for a content is based onat least a combination of individual recommendation scores for thecontent for each friend in an identified social network, wherein theindividual recommendation score is recursively determined to apredetermined degree of separation from the user in the identifiedsocial network; and providing a subset of the plurality of content tothe user based on the recommendation score of each of the plurality ofcontent fit each of the at least one social network.
 10. The networkcomputer of claim 9, wherein providing the subset of the plurality ofcontent to the user further comprises: ranking the plurality of contentseparately for each of the at least one social network based on therecommendation scores of each of the plurality of content; and providinga predetermined number of highest ranking content for each socialchannel to the user.
 11. The network computer of claim 9, furthercomprising determining the subset of the plurality of content based on aweight of each of the at least one social network and therecommendations scores for the plurality of content for each of the atleast one social network.
 12. The network computer of claim 9, whereinthe recommendation score of content for a social network is based on acombination of an intrinsic value of the content to the user and asocial value of the content that is based on the combination of theindividual recommendation scores for each friend in the social network.13. The network computer of claim 9, wherein determining therecommendation score of content for a social network further comprises,weighting at least an intrinsic value based on an importance to the userof the user's personal interests in comparison to social interests ofthe social network.
 14. The network computer of claim 9, wherein theindividual recommendation score for each respective friend is weightedbased on at least an influence of the respective friend on the user. 15.The network computer of claim 9, wherein the individual recommendationscore for each respective friend is combined with an interestprobability that both the respective friend and the user perform anaction on content.
 16. A processor readable non-transitory storage mediathat includes instructions for managing a plurality of content fordisplay to a user, wherein execution of the instructions by a processorenables actions, comprising: identifying at least one social networkassociated with the user; and for each of the at least one socialnetwork, determining a recommendation score for each of a plurality ofcontent for the user, wherein the recommendation score for a content isbased on at least a combination of individual recommendation scores forthe content for each friend in an identified social network, wherein theindividual recommendation score is recursively determined to apredetermined degree of separation from the user in the identifiedsocial network; and providing a subset of the plurality of content tothe user based on the recommendation score of each of the plurality ofcontent for each of the at least one social network.
 17. The media ofclaim 16, wherein providing the subset of the plurality of content tothe user further comprises: ranking the plurality of content separatelyfor each of the at least one social network based on the recommendationscores of each of the plurality of content; and providing apredetermined number of highest ranking content for each social channelto the user.
 18. The media of claim 16, further comprising determiningthe subset of the plurality of content based on a weight of each of theat least one social network and the recommendations scores for theplurality of content for each of the at least one social network. 19.The media of claim 16, wherein the recommendation score of content for asocial network is based on a combination of an intrinsic value of thecontent to the user and a social value of the content that is based onthe combination of the individual recommendation scores for each friendin the social network.
 20. The media of claim 16, wherein determiningthe recommendation score of content for a social network furthercomprises, weighting at least an intrinsic value based on an importanceto the user of the user's personal interests in comparison to socialinterests of the social network.
 21. The media of claim 16, wherein theindividual recommendation score for each respective friend is weightedbased on at least an influence of the respective friend on the user. 22.The media of claim 16, wherein the individual recommendation score foreach respective friend is combined with an interest probability thatboth the respective friend and the user perform an action on content.23. A system for managing a plurality of content for display to a user,comprising: a score determination computer that is operative to identifyat least one social network associated with the user, and for each ofthe at least one social network, determine a recommendation score foreach of a plurality of content for the user, wherein the recommendationscore for a content is based on at least a combination of individualrecommendation scores for the content for each friend in an identifiedsocial network, wherein the individual recommendation score isrecursively determined to a predetermined degree of separation from theuser in the identified social network; and a content selection computerthat is operative to provide a subset of the plurality of content to theuser based on the recommendation score of each of the plurality ofcontent for each of the at least one social network.
 24. The system ofclaim 23, wherein the content selection computer is operative to rankthe plurality of content separately for each of the at least one socialnetwork based on the recommendation scores of each of the plurality ofcontent, and to provide a predetermined number of highest rankingcontent for each social channel to the user.
 25. The system of claim 23,wherein the content selection computer is operative to determine thesubset of the plurality of content based on a weight of each of the atleast on social network and the recommendations scores for the pluralityof content for each of the at least one social network.
 26. The systemof claim 23, wherein the recommendation score of content for a socialnetwork is based on a combination of an intrinsic value of the contentto the user and a social value of the content that is based on thecombination of the individual recommendation scores for each friend inthe social network.
 27. The system of claim 23, wherein the scoredetermination computer is operative to weight at least an intrinsicvalue based on an importance to the user of the user's personalinterests in comparison to social interests of a social network todetermine the recommendation score of content for the social networkfurther comprises.
 28. The system of claim 23, wherein the scoredetermination computer is operative to determine an intrinsic value ofeach of the plurality of content based on at least one action that waspreviously taken by the user on other content that is similar tocorresponding content, and to employ the intrinsic value to determinethe recommendation score.
 29. The system of claim 23, wherein theindividual recommendation score for each respective friend is weightedbased on at least an influence of the respective friend on the user. 30.The system of claim 23, wherein the individual recommendation score foreach respective friend is combined with an interest probability thatboth the respective friend and the user perform an action on content.